diff --git a/code/NER_Model.py b/code/NER_Model.py
index 687a2de..9fc3afd 100644
--- a/code/NER_Model.py
+++ b/code/NER_Model.py
@@ -350,15 +350,18 @@ def predict(sentence, correct_spellings=True):
 
     output_str = crf.predict([feat])
     print(f" IOB Tagging : {output_str}")
-    output_str = NER_Model_postprocessing.tag_color(output_str)
-    output_str = NER_Model_postprocessing.tag_price(output_str)
-    output_str = NER_Model_postprocessing.tag_shape(output_str)
-    output_str = NER_Model_postprocessing.tag_size(output_str)
-
-    print(f"Postprocessed output: {output_str}")
     result = []
+
     for token, tag in zip(tokens, output_str[0]):
         result.append((token, tag))
+    
+    result = NER_Model_postprocessing.tag_color(result)
+    result = NER_Model_postprocessing.tag_price(result)
+    result = NER_Model_postprocessing.tag_shape(result)
+    result = NER_Model_postprocessing.tag_size(result)
+
+    print(f"Postprocessed output: {result}")
+
     return result
 
 def evaluate_results(token_list):
diff --git a/code/evaluation.py b/code/evaluation.py
index ac4ccdf..5e420e9 100644
--- a/code/evaluation.py
+++ b/code/evaluation.py
@@ -200,6 +200,7 @@ def evaluate(true_seqs, pred_seqs, verbose=True):
 tokens = []
 actual_tags = []
 predicted_tags = []
+evaluation_res = {"Token":[], "Actual Tag":[], "Predicted Tag":[]}
 NER_Model.main()
 for index, row in validataion_df.iterrows():
     if row['Tokens'] is np.nan:
@@ -209,18 +210,24 @@ def evaluate(true_seqs, pred_seqs, verbose=True):
         pred_tok_tag = Negation_Analyser.predict(predicted_ner, "tags")
         for tag in pred_tok_tag['token-tags']:
             predicted_tags.append(tag[1])
+            evaluation_res["Predicted Tag"].append(tag[1])
         print(f"Predicted : {pred_tok_tag}")
         sent = []
         tok_tag_act = []
 
     else:
         sent.append(row['Tokens'])
+        evaluation_res["Token"].append(row['Tokens'])
         if(row['is_negative'] is True):
             tok_tag_act.append((row['Tokens'], row['Tags'].replace('I-', 'I-N-')))
             actual_tags.append(row['Tags'].replace('I-', 'I-N-'))
+            evaluation_res["Actual Tag"].append(row['Tags'].replace('I-', 'I-N-'))
         else:
             actual_tags.append(row['Tags'])    
             tok_tag_act.append((row['Tokens'], row['Tags']))
+            evaluation_res["Actual Tag"].append(row['Tags'])
 
 
-evaluate(actual_tags, predicted_tags)
\ No newline at end of file
+evaluate(actual_tags, predicted_tags)
+res_df = pd.DataFrame(evaluation_res)
+res_df.to_csv('data/combined_evaluation.csv',index=False)
\ No newline at end of file
diff --git a/data/combined_evaluation.csv b/data/combined_evaluation.csv
new file mode 100644
index 0000000..9b46f4c
--- /dev/null
+++ b/data/combined_evaluation.csv
@@ -0,0 +1,2628 @@
+Token,Actual Tag,Predicted Tag
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+",",O,I-SIZE
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+i,O,I-PRODUCT
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+42,I-SIZE,I-SIZE
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+i,O,O
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+a,O,O
+rod,I-N-ATTRIBUTE,I-N-ATTRIBUTE
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+10,I-SIZE,I-SIZE
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+size,I-SIZE,I-SIZE
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+'',I-SIZE,I-SIZE
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+",",O,O
+i,O,O
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+fat,I-PRODUCT,O
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+lacrosse,I-N-ATTRIBUTE,I-N-ATTRIBUTE
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+with,O,O
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+soy,I-N-ATTRIBUTE,I-N-ATTRIBUTE
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+i,O,O
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+.,O,O
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+is,O,O
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+blue,I-COLOUR,I-COLOUR
+and,O,O
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+",",O,O
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+is,O,O
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+2,I-SIZE,I-SIZE
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+22,I-SIZE,I-N-SIZE
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+cases,I-PRODUCT,I-PRODUCT
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+trousers,I-PRODUCT,I-PRODUCT
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+38,I-SIZE,I-SIZE
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+leg,I-SIZE,I-SIZE
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+unit,I-PRODUCT,I-PRODUCT
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+10,I-SIZE,I-SIZE
+'',I-SIZE,I-SIZE
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+toilet,I-PRODUCT,I-PRODUCT
+asap,O,O
+",",O,O
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+'',I-SIZE,I-N-SIZE
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+16,I-N-SIZE,I-N-SIZE
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+42,I-SIZE,I-SIZE
+'',I-SIZE,I-SIZE
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diff --git a/resources/Screenshot 2022-06-26 at 23.13.59.png b/resources/Screenshot 2022-06-26 at 23.13.59.png
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